{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/software-and-application-patterns-for","title":"Software and application patterns for explanation methods","arxiv_id":"1904.04734","date":"2019-04-09","proceeding":null,"authors":["Maximilian Alber"],"abstract":"Deep neural networks successfully pervaded many applications domains and are\nincreasingly used in critical decision processes. Understanding their workings\nis desirable or even required to further foster their potential as well as to\naccess sensitive domains like medical applications or autonomous driving. One\nkey to this broader usage of explaining frameworks is the accessibility and\nunderstanding of respective software. In this work we introduce software and\napplication patterns for explanation techniques that aim to explain individual\npredictions of neural networks. We discuss how to code well-known algorithms\nefficiently within deep learning software frameworks and describe how to embed\nalgorithms in downstream implementations. Building on this we show how\nexplanation methods can be used in applications to understand predictions for\nmiss-classified samples, to compare algorithms or networks, and to examine the\nfocus of networks. Furthermore, we review available open-source packages and\ndiscuss challenges posed by complex and evolving neural network structures to\nexplanation algorithm development and implementations.","url_abs":"http://arxiv.org/abs/1904.04734v1","url_pdf":"http://arxiv.org/pdf/1904.04734v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"software-and-application-patterns-for","repo_url":"https://github.com/albermax/interpretable_ai_book__sw_chapter","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}